Our architecture solves the problem of transformation invariance by introducing transformations such that output for a particular object remains the same even after rotational or translation transforms. The invariance problem is addressed by input and feature transformations sub-networks that normalize the pose for the input point cloud. The network applies transformations on the input point cloud similar to spatial transformers [1]} to achieve pose normalization. A geometric transformation is equivalent to multiplying each point with a transformation matrix.
